The following comment refers to this/these guideline(s)
Methods and standards
To answer research questions, researchers use scientifically sound and appropriate methods. When developing and applying new methods, they attach particular importance to quality assurance and the establishment of standards.
The application of a method normally requires specific expertise that is ensured, where necessary, by suitable cooperative arrangements. The establishment of standards for methods, the use of software, the collection of research data and the description of research results is essential for the comparability and transferability of research outcomes.
Researchers document all information relevant to the production of a research result as clearly as is required by and is appropriate for the relevant subject area to allow the result to be reviewed and assessed. In general, this also includes documenting individual results that do not support the research hypothesis. The selection of results must be avoided. Where subject-specific recommendations exist for review and assessment, researchers create documentation in accordance with these guidelines. If the documentation does not satisfy these requirements, the constraints and the reasons for them are clearly explained. Documentation and research results must not be manipulated; they are protected as effectively as possible against manipulation.
An important basis for enabling replication is to make available the information necessary to understand the research (including the research data used or generated, the methodological, evaluation and analytical steps taken, and, if relevant, the development of the hypothesis), to ensure that citations are clear, and, as far as possible, to enable third parties to access this information. Where research software is being developed, the source code is documented.
Notes on documentation when using computer-assisted algorithmic or data-driven procedures in research
When using primarily computer-based algorithmic (e.g. tracking methods) or data-driven methods (e.g. machine learning) in research, academic researchers should appropriately document the methods used, their parameterisation, training and verification data sets, if applicable, as well as the research data used for the results presented, and make these accessible if required. This should be embedded in a verifiable research process which is also documented. Publication of the aforementioned documentations is desirable. Insights gained in this way – just like those gained using conventional methods – should be classified in a scholarly fashion and (further) reviewed by the academic community.
The comment belongs to the following categories:
GL11 (General) , GL12 (General)